Conventional Radar Research Articles

The principle of operation of an autodyne optoelectronic transceiver of short-range radar systems

The description of the device and the principle of operation of a new type of autodyne transceiver of short-range radar systems (SRRS), made on the basis of an optoelectronic oscillator (OEO), is presented. The antenna is connected to the OEO through a dividing-decoupling device (DDD), which provides, on the one hand, conditions for generating self-oscillations in the system and, on the other hand, transmitting and receiving radio signals with minimal losses. The scheme of the DDD and the method of calculating their parameters are considered. A mathematical model of the autodyne system based on the OEO has been developed, and the basic relations for determining its parameters and characteristics have been obtained. It is demonstrated that the use of OEO provides a significant increase in the equivalent Q-factor of the oscillator system and the reduction in its own noise. The improvement of these parameters of the autodyne SRRS will significantly increase the range of operation, expand the range of operating frequencies and the scope of their application. The prospects of using autodynes based on OEO in the creation of detection systems of objects with a small effective scattering area, such as unmanned aerial vehicles, for protection against which the use of conventional (distant) radars is ineffective, are noted.

Motion Estimation and Compensation in Automotive MIMO SAR

With the advent of self-driving vehicles, autonomous driving systems will have to rely on a vast number of heterogeneous sensors to perform dynamic perception of the surrounding environment. Synthetic Aperture Radar (SAR) systems increase the resolution of conventional mass-market radars by exploiting the vehicle’s ego-motion, requiring very accurate knowledge of the trajectory, usually not compatible with automotive-grade navigation systems. In this setting, radar data are typically used to refine the navigation-based trajectory estimation with so-called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">autofocus</i> algorithms. Although widely used in remote sensing applications, where the timeliness of the imaging is not an issue, autofocus in automotive scenarios calls for simple yet effective processing options to enable real-time environment imaging. This paper aims at providing a comprehensive theoretical and experimental analysis of the autofocus <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">requirements</i> in typical automotive scenarios. We analytically derive the effects of navigation-induced trajectory estimation errors on SAR imaging, in terms of defocusing and wrong targets’ localization. Then, we propose a motion estimation and compensation workflow tailored to automotive applications, leveraging a set of stationary Ground Control Points (GCPs) in the low-resolution radar images (before SAR focusing). We theoretically discuss the impact of the GCPs position and focusing height on SAR imaging, highlighting common pitfalls and possible countermeasures. Finally, we show the effectiveness of the proposed technique employing experimental data gathered during open road campaign by a 77 GHz multiple-input multiple-output radar mounted in a forward-looking configuration.

Spectral Algorithm in Waves Profiling and Prediction From Radar Backscatter

Conventional X-band radars constitute a convenient cheap source of measurements for deterministic sea wave profiling and prediction. The radar backscatter measurements, however, suffer from several imperfections such as the effects of larger waves shadowing smaller waves. In order to extract the wave profiles and build a reliable (spectral) sea model, multiple radar scans must be processed. A technique based on 3DFFT has been proven to show promising results and has been implemented in commercially available wave profiling radars. Nonetheless, there are several drawbacks associated with using 3DFFT to determine the phase-resolved spectral coefficients in the sea model from a number of radar scans. In this article, we present an alternative spectral approach that can offer several advantages over 3DFFT in radar wave profiling and prediction. First, it has the ability to directly handle ship movement and radar rotation antenna effects with no approximation, interpolation, or extra cost. Second, it can offer an increase in the output update rate by eliminating repeating computations, which is achieved by its recursive nature (i.e., using past output) and by its multistep architecture. Third, it can be set to readily target the relative spectral components that can best describe the wave field. The proposed technique is evaluated using raw backscatter field data from a dedicated sea trial. A simulation study is also included to gain more understanding about the performance of the proposed technique in particular and radar wave sensing and prediction in general.

System Concepts and Potential Applications of a Tri-Beam Spaceborne SAR Mission

Multitemporal synthetic aperture radar interferometry (MT-InSAR), capable of detecting both surface deformation and elevation with high precision, is used for many applications in earth observation. Conventional synthetic aperture radar (SAR) missions with a single beam only detect deformation along the line of sight (LOS) and relative elevation due to the undetermined model of phase wrapping. In a multisatellite SAR mission, measurements from different SAR geometry improve the sensitivity of the detectable deformation, especially to the deformation along the north–south (N-S) direction. However, it is difficult to combine the measurements from varying viewing angles since the absolute phase cannot be reconstructed without a ground control point. In this article, a tri-beam SAR system is introduced to detect 3-D deformation and derive multiview 3-D surface model from a single spaceborne platform. The accuracy of the 3-D deformation from the tri-beam SAR is exploited with varying squint and incident angles to obtain the optimal parameters of the three beams. Then a multidimensional coherent scattering model is used to simulate the multitemporal SAR data with different viewing angles. Regarding the tri-beam SAR, potential applications in earth observation including 3-D deformation monitoring, geodetic stereo SAR, and multiview 3-D forest reconstruction are investigated subsequently. The results of this study indicate that the tri-beam SAR is able to measure 3-D deformation and reconstruct 3-D surface model without ground control point.

A Transmitter Design for the Multi-beam CC-OFDM Azimuth Scanning MIMO Radar

A conventional phased array radar provides a high gain by transmitting coherent signals over a large number of antenna elements. However, it does not provide a full time-on-target because it produces beams that scan the whole angular view of the radar. This paper describes an implementation of circulating codes (CC) in azimuth scanning MIMO radar that adopts OFDM waveform. The CC MIMO technique can produce a beam toward a certain angle by applying an appropriate delay difference between the MIMO antennas that transmit the same waveform across the azimuth. The adoption of OFDM waveform in the CC MIMO radar, gives the possibility to apply the phase difference for beamforming in the frequency domain of the OFDM signals. CC-OFDM MIMO with spectrum division into sub-bands and use of orthogonal codes, allow for simultaneous transmission of orthogonal OFDM symbols that generate orthogonal beams toward different angles. This strategy makes it possible for the radar system to have multiple and simultaneous beamforming to provide a full-time coverage. Furthermore, the transmission of a series of orthogonal OFDM symbols in each beam enables the detection of long-range targets. By taking as an example the design of long-range surveillance radar, the paper discusses further issues pertinent to the implementation of the CC OFDM MIMO radar, including the beam squinting problem and its remedy, arrangement of the multiple beams, the radar system structure, use of Golay codes for orthogonalization and PAPR mitigation, use of software-defined radar that eliminates the need of amplitude and phase control in each transmitter, transmit scheduling for multi-beam long-range target detection, and multidimensional ambiguity function analysis. The latter indicates that the CC OFDM MIMO radar can achieve a high resolution in angle, range, and velocity, with beam isolation of -30 dB for neighboring beams and co-channel beams.

Structure-Guaranteed SAR Imagery via Spatially-Variant Morphology Regularization in ADMM Manner

Conventional sparsity-driven synthetic aperture radar (SAR) imagery proceeds via &#x2113;₁ regularization, or named by least absolute shrinkage and selection operator (LASSO). However, followed by the enhanced sparse feature, structures of the scenes or targets of interests in weak scattering are easily lost. Therefore, it becomes difficult to make use of the high-resolution SAR data, even high costs have been paid for the resolution. In this paper, a novel structure-guaranteed SAR (SG-SAR) imaging algorithm is proposed by utilizing the morphology metric for the cluster feature of the scatterers of the scenes/targets of interests. By introducing structural prior in terms of morphology norm, the intended structure features can be highlighted via convex regularization. More specifically, to accommodate to complicated scenarios or targets, the structure element in the morphology regularizer is designed to be spatially variant under structure tensor representation. Different from conventional convex optimizations, the proposed SG-SAR algorithm is solved under alternating direction method of multipliers (ADMM), which is flexible to incorporate with the super-resolution imagery. In such cases, both sparse and structural features can be simultaneously enhanced, even with limited measurements and in low signal-to-noise ratio (SNR). Superior convergence and robustness can be guaranteed. Moreover, a grouping mask scheme is used to accommodate to the complex-valued SAR data. Finally, both simulated and measured SAR data are applied for the validation. Comparisons with the conventions are performed in terms of phase transition analysis, so as to verify the superiority of the proposed algorithm both qualitatively and quantitatively.

Structure-Awareness SAR Imagery by Exploiting Structure Tensor TV Regularization Under Multitask Learning Framework

Conventional sparsity-driven synthetic aperture radar (SAR) imagery often encounters the problem of loss of structural features in weak scattering. Although there are algorithms that focus on structure enhancement, no proper balance between accuracy and efficiency can be achieved. In this article, a novel feature enhancement algorithm, named structure-awareness SAR (SA-SAR), is proposed by exploiting an emerging regularizer of structure tensor total variation (STV). By imposing the STV norm onto the prior of the scenes or targets to be imaged, the intended structure feature can be analytically solved under the proximal algorithm. More specifically, the regularization method is developed within the alternating direction method of multipliers (ADMM) framework, where closed-form proximity operators can be derived. Due to the ADMM framework, it facilitates to incorporate with more features to be enhanced in a fully synergistic way. Therefore, the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\ell _{1}$ </tex-math></inline-formula> and entropy norms are involved so that the sparse and focusing features can be enhanced accordingly. Considering the coherence of the SAR data, a linear proximal operator is developed within the multitask learning framework. The unavoidable error propagations can be alleviated in a great extent. In such cases, the proposed algorithm is superior in terms of convergence and efficiency. To facilitate the implementation and computation, the STV proximal mapping is optimized under the Vieta theorem. Finally, both simulated and raw SAR data are applied to verify the effectiveness of the proposed algorithm. Comparisons with conventional algorithms are carried out to show the superiority of the proposed algorithm.

Air Moving Target Imaging for Staggered ISAR

The rapid development of the modern electronic counter-countermeasures (ECCMs) has made the conventional inverse synthetic aperture radar (ISAR) associated with the regular signal waveforms more vulnerable and unreliable. To deal with this issue, the complex waveform designment with staggered pulse sampling is developed in an ISAR system in this letter. In the proposed method, the generalized time-scaled transform (GTST) is adopted to effectively accomplish the irregular signal reconstruction and linear phase decoupling. After that, a set of matched filtering functions are established and interspersed in the imaging processes to accomplish the subsequent motion compensation, target imaging, and range and cross-range scaling. Finally, a satisfied ISAR imagery corresponding to the real size of a moving target can be recovered. The simulation and real-measured radar data processing results are applied to demonstrate the effectiveness of the proposed method.

Adaptive Pulse Compression for Sidelobes Reduction in Stretch Processing Based MIMO Radars

Multiple-Input Multiple-Output (MIMO) radars provide various advantages as compared to conventional radars. Among these advantages, improved angular diversity feature is being explored for future fully autonomous vehicles. Improved angular diversity requires use of orthogonal waveforms at transmit as well as receive sides. This orthogonality between waveforms is critical as the cross-correlation between signals can inhibit the detection of weaker targets due to sidelobes of stronger targets. This paper investigates the Reiterative Minimum Mean Squared Error (RMMSE) mismatch filter design for range sidelobes reduction for a Slow-Time Phase-Coded (ST-PC) Frequency Modulated Continuous Wave (FMCW) MIMO radar. Initially, the performance degradation of RMMSE filter is analyzed for improperly decoded received pulses. It is then shown mathematically that proper decoding of received pulses requires phase compensation related to any phase distortions caused due to doppler and spatial locations of targets. To cater for these phase distortions, it is proposed to re-adjust the traditional order of operations in radar signal processing to doppler, angle and range. Additionally, it is also proposed to incorporate sidelobes decoherence for further suppression of sidelobes. This is achieved by modification of the structured covariance matrix of baseline single-input RMMSE mismatch filter. The modified structured covariance matrix is proposed to include the range estimates corresponding to each transmitter. These proposed modifications provide additional sidelobes suppression while it also provides additional fidelity for target peaks. The proposed approach is demonstrated through simulations as well as field experiments. Superior performance in terms of range sidelobes suppression is observed when compared with baseline RMMSE and traditional Hanning windowed range response.

Interferometric coherence and seasonal deformation characteristics analysis of saline soil based on Sentinel-1A time series imagery

Affected by the natural environmental and human activity factors, significant seasonal differences appear on the regional scattering characteristic and ground deformation of saline soil. Interferometric decorrelation due to season replacement limits the conventional multi-temporal interferometric synthetic aperture radar (MT-InSAR) technique and its application in such areas. To extend the monitoring capability in the salt desert area, we select a vast basin of saline soil around Howz-e-Soltan Salt Lake of Iran as the study area and present an improved MT-InSAR for experimental research. Based on 131 C-band Sentinel-1A images collected between October 2014 to July 2020,1896 refined interferograms in total are selected from all interfero-gram candidates. Interferometric coherence analysis shows that the coherence in the saline soil area has an apparent seasonal variation, and the soil moisture affected by the precipitation may be the main factor that leads to the seasonal variation. Subsequently, the deformation characteristics of saline soil under different environmental conditions and human activity factors are compared and analyzed in detail. Related deformation mechanisms of different saline soil types are initially revealed by combining interferometric coherence, meteorological data, and engineering geological characteristics of saline soil. Related results would provide reference for the large-scale infrastructure construction engineering in similar saline soil areas.

Frequency-domain imaging algorithm based on time-frequency-domain perturbation for maneuvering highly squinted FMCW-SAR

Serious range-azimuth coupling and spatial variability of imaging parameters limit the focusing depth of conventional synthetic aperture radar (SAR) imaging methods for maneuvering high-squint frequency modulated continuous wave -SAR. To expand the effective imaging area, an imaging method based on time-frequency-domain perturbation is proposed. First, the slant range model based on data recording parameters is optimized to remove Doppler frequency shift and secondary coupling caused by in-pulse movement. Then, the spatial variability of range cell migration (RCM) is corrected by constructing the time-domain perturbation function to achieve unified RCM correction. In azimuth processing, the linear spatial variability of the first- to third-order Doppler parameters and the second-order space variation of azimuth modulation frequency are removed by the time-frequency perturbation filtering functions. The focus quality of the nonscene center is effectively improved. Finally, the effectiveness of the algorithm is verified by both the simulation data and the measured data.

Coherent detection-based photonic radar for autonomous vehicles under diverse weather conditions.

Autonomous vehicles are regarded as future transport mechanisms that drive the vehicles without the need of drivers. The photonic-based radar technology is a promising candidate for delivering attractive applications to autonomous vehicles such as self-parking assistance, navigation, recognition of traffic environment, etc. Alternatively, microwave radars are not able to meet the demand of next-generation autonomous vehicles due to its limited bandwidth availability. Moreover, the performance of microwave radars is limited by atmospheric fluctuation which causes severe attenuation at higher frequencies. In this work, we have developed coherent-based frequency-modulated photonic radar to detect target locations with longer distance. Furthermore, the performance of the proposed photonic radar is investigated under the impact of various atmospheric weather conditions, particularly fog and rain. The reported results show the achievement of significant signal to noise ratio (SNR) and received power of reflected echoes from the target for the proposed photonic radar under the influence of bad weather conditions. Moreover, a conventional radar is designed to establish the effectiveness of the proposed photonic radar by considering similar parameters such as frequency and sweep time.

Noncontact Vital Sign Sensing Under Nonperiodic Body Movement Using a Novel Frequency-Locked-Loop Radar

Most conventional vital-sign radars cannot easily detect human vital sign signals under nonperiodic body movement. This article presents a novel frequency-locked loop (FLL) radar that can track the phase changes that are caused by nonperiodic body movement. A gain/phase detector and a cross correlation function (CCF) in the FLL radar greatly reduce the second harmonic, the intermodulation terms between vital sign signals and nonperiodic body movement noise, enhancing both the signal-to-noise ratio and the accuracy of detection of the vital signs. Measurements indicated that the implemented 2.4-GHz FLL radar with a gain/phase detector and CCF can effectively detect the human respiration rate (RR) and heart rate (HR) under nonperiodic body movement. Using a chest belt as the measurement reference, the standard deviation (SD) of RR and HR measurements were 1.27 and 5.69 beats/min, respectively, when the seated subject under test (SUT) swung nonperiodically his upper body. When the SUT walked away from the radar naturally at 2.6 km/h, the implemented FLL radar measured RR and HR with SDs of 2.67 and 8.28 beats/min, respectively.

Ground Moving Target Imaging for Highly Squint SAR by Modified Minimum Entropy Algorithm and Spectrum Rotation

Ground moving target (GMT) is displaced and defocused in conventional synthetic aperture radar (SAR) image due to the residual phase error of non-cooperative GMT motion. In this study, a GMT imaging (GMTIm) method is proposed for highly squint SAR. As the squint angle become large, the displace and defocus effect of the GMT image become severe and the geometry distortion of the GMT image cannot be ignored. The proposed method first deduced the two-dimensional (2-D) frequency domain signal of the GMT and the bulk compression function of the Range Migration Algorithm (RMA) in highly squint SAR. Then GMT ROI data are extracted and a modified minimum entropy algorithm (MMEA) is proposed to refocus the GMT image. MMEA introduces the idea of bisection into the iteration process to converge more efficiently than the previous minimum entropy method. To overcome the geometry distortion of the GMT image, an equivalent squint angle spectrum rotation method is proposed. Finally, to suppress the GMT image sidelobe, the sparse characteristic of GMT is considered and a sparse enhancement method is adopted. The proposed method can realize GMTIm in highly squint SAR where the squint angle reaches to 75 degrees. The PSNR and ISLR of point target in highly squint SAR is close to that in side-looking SAR. The simulated point target data and ship data are used to validate the effectiveness of the proposed method.

Super-resolution performance studies for orbital-angular-momentum-based imaging radar

ABSTRACT Vortex electromagnetic (EM) wave carrying orbital angular momentum (OAM), whose unique wavefront distribution exhibits angular diversity characteristics, which provides a richer degree of freedom for information modulation. Although several works have reported that the EM vortex imaging has superior performance in target detection and imaging with azimuthal super-resolution, the underlying physical mechanism needs to be further developed. This article offers a solution to significantly refine the imaging resolution for conventional real-aperture imaging radar (CRAIR) using multiple OAM-carrying beams. Firstly, it is deduced that the various spatial frequency distributions and time-varying phase wavefront distributions of OAM beams promote the realization of OAM-based imaging radar (OAMIR) super-resolution imaging. Secondly, a scheme for adjusting the directivity of EM vortex beam is proposed, which can effectively acquire most spatial frequency components of the probed target, resulting in the target can be illuminated simultaneously by the main lobes of various beams carrying different topological charges. Thirdly, the point-spread function (PSF) comparison for CRAIR and OAMIR is carried out by theoretical analysis and numerical simulation, and the spatial azimuth resolution for OAMIR is derived. Finally, the signal mathematical model for OAMIR is established and analysed. Simulation results validate that the proposed OAM-based imaging paradigm breaks through the Rayleigh limit associated with conventional real-aperture imaging technology and achieves imaging performance superior to OAMIR in previous works.

Knowledge‐aided block sparse Bayesian learning STAP for phased‐array MIMO airborne radar

The phased-array multiple-input multiple-output (PA-MIMO) airborne radar faces more severe sample shortage problem than the conventional PA radar. Hence, it suffers from severe performance degradation when it adopts the traditional space-time adaptive processing (STAP) methods. Fortunately, the introduction of sparse recovery (SR) brings a novel perspective to reducing the sample requirement. However, most existing SR-STAP methods only consider the clutter sparsity as prior information. In fact, more useful information, such as radar parameters, ground environment, airplane state, can all be obtained in advance and further utilised to improve the performance of SR-STAP. In this study, the clutter structure, obtained based on the abovementioned prior knowledge, is utilised in the block SR-STAP in the PA-MIMO airborne radar. The clutter suppression performance is poor when the block SR algorithm is directly applied into the STAP because the clutter structure is destroyed by common vectorisation operation. To solve this problem, the vectorisation operation is first redesigned based on the clutter distribution characteristics and a novel dictionary is constructed. A basic block sparse Bayesian learning STAP scheme is then proposed in the PA-MIMO radar’s application. The numerical experiments illustrate that the proposed method can achieve satisfactory performance with a few training samples and outperforms the state-of-the-art SR-STAP methods.

Low-PAPR Waveforms with Shaped Spectrum for Enhanced Low Probability of Intercept Noise Radars

Noise radars employ random waveforms in their transmission as compared to traditional radars. Considered as enhanced Low Probability of Intercept (LPI) radars, they are resilient to interference and jamming and less vulnerable to adversarial exploitation than conventional radars. At its simplest, using a random waveform such as bandpass Gaussian noise as a probing signal provides limited radar performance. After a concise review of a particular noise radar architecture and related correlation processing, this paper justifies the rationale for having synthetic (tailored) noise waveforms and proposes the Combined Spectral Shaping and Peak-to-Average Power Reduction (COSPAR) algorithm, which can be utilized for synthesizing noise-like sequences with a Taylor-shaped spectrum under correlation sidelobe level constraints and assigned Peak-to-Average-Power-Ratio (PAPR). Additionally, the Spectral Kurtosis measure is proposed to evaluate the LPI property of waveforms, and experimental results from field trials are reported.

Three-Dimensional Parameter Estimation of Uniform Circular Frequency Diverse Array Radar With Two-Stage Estimator

Frequency diverse array (FDA) radar has been studied by many researchers due to the range-angle dependent beampattern which is different from the conventional phased array radar. However, the contribution of previous studies has received little attention within the circular frequency diverse array radar and no known empirical research has focused on investigating the three-dimensional (3D) parameter estimation (azimuth, elevation, and range). In this paper, we proposed a two-stage estimation algorithm for 3D parameter estimation of uniform circular frequency diverse array (UC-FDA) radar and firstly demonstrate the capability and performance of 3D parameter estimation. The first step of the two-stage estimation is the estimation of azimuth and elevation from the beamspace which is transformed from the element space by the beamformer. The second step is to substitute the angle obtained in the first step into the transmitting steering matrix for the range estimation. Cramér-Rao bound (CRB) that is used to evaluate the 3D parameter estimation performance is derived to compare. Theoretical analysis and numerical simulation demonstrate that the UC-FDA radar has superior 3D parameter estimation performance and the effectiveness of the proposed two-stage estimation algorithm. The research findings can contribute to a better 3D localization method and represent an additional step towards developing multi-dimensional localization and interference suppression for UC-FDA radar.

Cooperative Fusion Based Passive Multistatic Radar Detection.

Passive multistatic radars have gained a lot of interest in recent years as they offer many benefits contrary to conventional radars. Here in this research, our aim is detection of target in a passive multistatic radar system. The system contains a single transmitter and multiple spatially distributed receivers comprised of both the surveillance and reference antennas. The system consists of two main parts: 1. Local receiver, and 2. Fusion center. Each local receiver detects the signal, processes it, and passes the information to the fusion center for final detection. To take the advantage of spatial diversity, we apply major fusion techniques consisting of hard fusion and soft fusion for the case of multistatic passive radars. Hard fusion techniques are analyzed for the case of different local radar detectors. In terms of soft fusion, a blind technique called equal gain soft fusion technique with random matrix theory-based local detector is analytically and theoretically analyzed under null hypothesis along with the calculation of detection threshold. Furthermore, six novel random matrix theory-based soft fusion techniques are proposed. All the techniques are blind in nature and hence do not require any knowledge of transmitted signal or channel information. Simulation results illustrate that proposed fusion techniques increase detection performance to a reasonable extent compared to other blind fusion techniques.

A reduced-dimension RML method for transmit beamspace-based MIMO radar

In this paper, the problem of low angle estimation for multiple-input multiple-output (MIMO) radar is considered. Though the refined maximum likelihood (RML) algorithm can be directly applied to low angle estimation in MIMO radar, its computational burden is huge due to the large virtual aperture. Moreover, the conventional MIMO radar employs full waveform diversity, which actually sacrifices the emission gain. To reduce the computational burden and improve the transmit gain, a reduced-dimension RML method for transmit beamspace-based (TB-based) MIMO radar is developed. Firstly, the transmit beamspace (TB) matrix is designed to focus the transmit energy within the desired spatial sector. Then, to reduce the computational complexity and retain the estimation precision of the direct RML (D-RML) method for TB-based MIMO radar, a transmit-receive separate RML (TRS-RML) method is developed by applying the RML algorithm to the transmit and receive side respectively. Moreover, we propose to fuse the estimation results of the transmit and receive side together to improve the estimation precision. Theoretical analysis shows the TRS-RML method requires much lower computational burden than D-RML method. Numerical experiments show the proposed method has higher estimation accuracy compared with the D-RML method for the conventional MIMO radar in most angle region.